Comparative Analysis aimed at optimization and visualization of BIM for VR applications.
Bibliographic record
Abstract
In this thesis, we explore the proper visualization of Autodesk Revit data inside virtual reality applications by presenting a new optimization framework leveraging a custom plugin / tool inside Autodesk 3DS Max.This tool leverages new Autodesk software integration APIs, in conjunction with Maxscript (native language of 3DS Max) to read and translate BIM data from Revit, while preserving its metadata, materials and textures.Multiple model optimization procedures are applied automatically with the most important being the implementation of a Level of Detail (LOD) system for every model in the scene.To test our proposed framework, we conducted a comparative analysis of quantitative data gathered from multiple virtual reality applications deployed and running inside an Oculus Quest mobile VR device.Four different Revit case studies were used to develop these applications.They were chosen to exemplify the different levels of complexity that a BIM project can reach in real situations.The first set of applications were developed using our proposed framework, while the second set were developed using the Unreal Datasmith toolset by Epic Games.Results show that the applications developed using our proposed framework performed better than the applications developed using the Unreal Datasmith toolset, in terms of higher average frames per second, higher visual fidelity and lower GPU usage.Additionally, results show that while the implementation of LOD systems requires extra memory, its benefits regarding performance are substantial.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".